{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 指数基本信息\n",
    "\n",
    "- 作者：DGC'Idea\n",
    "- 版本：V0.3   \n",
    "- 更新日期：2018年2月4日\n",
    "\n",
    "----------\n",
    "\n",
    "1. 基本信息：指数发布日期、编制机构、编制方法等信息；\n",
    "2. PE信息：指数PE最新值、百分位，各重要百分位点，PE折线图；\n",
    "3. PB信息：指数PB最新值、百分位，各重要百分位点，PE折线图；\n",
    "4. 财务信息：指数最近四个季度的营收、利润、净资产；\n",
    "5. 行情信息：指数最新收盘、开盘价、成交额等；\n",
    "6. 成份股信息：指数十大成份股信息；\n",
    "7. 对应基金信息：指数对应的优质基金信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'jy' from 'jqdatasdk' (C:\\ProgramData\\Anaconda3\\lib\\site-packages\\jqdatasdk\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-0439519e68f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtimedelta\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mjqdatasdk\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mjy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'jy' from 'jqdatasdk' (C:\\ProgramData\\Anaconda3\\lib\\site-packages\\jqdatasdk\\__init__.py)"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import timedelta,date\n",
    "from jqdatasdk import jy\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#引入自定义库\n",
    "import sys\n",
    "sys.path.append('../PF')\n",
    "sys.path.append('../TL')\n",
    "from tl import Code\n",
    "from pf_idx import *\n",
    "from pf_fnd import *\n",
    "\n",
    "index=Index('csv','../Data/')\n",
    "fund=Fund('csv','../Data/','index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#==================================================================================================================\n",
    "# 聚源常量\n",
    "#------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "#获取聚源常量、查询CT_SystemConst表\n",
    "def get_jydata_const(const_type):\n",
    "    df=pd.DataFrame()\n",
    "    #因jy每次最多返回3000条数据，所以要多次查询\n",
    "    #偏移值\n",
    "    offset=0    \n",
    "    while True:\n",
    "        q=query(\n",
    "            #内部代码\n",
    "            jy.CT_SystemConst.DM,\n",
    "            #常量值\n",
    "            jy.CT_SystemConst.MS,\n",
    "        ).filter(\n",
    "            #按照类别查询\n",
    "            jy.CT_SystemConst.LB==const_type,\n",
    "        #偏移         \n",
    "        ).offset(offset)\n",
    "        #查询    \n",
    "        temp_df=jy.run_query(q)  \n",
    "        if len(temp_df)==0:\n",
    "            break\n",
    "        #追加数据\n",
    "        df=df.append(temp_df)\n",
    "        #偏移值每次递增3000    \n",
    "        offset+=3000\n",
    "    #按照DM索引\n",
    "    df.index=df['DM']\n",
    "    #返回字典\n",
    "    return df['MS'].to_dict()\n",
    "\n",
    "#指数成份股权重\n",
    "WAMethod_const=get_jydata_const(1265)\n",
    "#指数类别常量\n",
    "IndexType_const=get_jydata_const(1266)\n",
    "#指数调整周期常量\n",
    "ComponentAdPeriod_const=get_jydata_const(1264)\n",
    "#证券市场常量\n",
    "SecuMarket_const=get_jydata_const(201)\n",
    "#板块常量\n",
    "ListedSector_const=get_jydata_const(207)\n",
    "\n",
    "   \n",
    "\n",
    "\n",
    "\n",
    "#==================================================================================================================\n",
    "# 成份股信息\n",
    "#------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "# 获取股票市值\n",
    "# InnerCode_list：内部代码列表\n",
    "# 返回数据表\n",
    "def get_stock_valuation(InnerCode_list):\n",
    "    df=pd.DataFrame()\n",
    "    #因jy每次最多返回3000条数据，所以要多次查询\n",
    "    today=date.today()\n",
    "    #偏移值\n",
    "    offset=0    \n",
    "    while True:\n",
    "        q=query(\n",
    "            #内部代码\n",
    "            jy.LC_DIndicesForValuation.InnerCode,\n",
    "            #流通市值\n",
    "            jy.LC_DIndicesForValuation.NegotiableMV,\n",
    "        ).filter(\n",
    "            #按照内部代码、交易日查询\n",
    "            jy.LC_DIndicesForValuation.InnerCode.in_(InnerCode_list),\n",
    "            jy.LC_DIndicesForValuation.TradingDay==today,\n",
    "        ).offset(\n",
    "            #偏移  \n",
    "            offset\n",
    "        )\n",
    "        temp_df=jy.run_query(q)  \n",
    "        if len(temp_df)==0:\n",
    "            break\n",
    "        #追加数据\n",
    "        df=df.append(temp_df)\n",
    "        #偏移值每次递增3000    \n",
    "        offset+=3000  \n",
    "    if len(df)!=0:\n",
    "        #按照内部代码索引\n",
    "        df.index=df['InnerCode']\n",
    "        del df['InnerCode']\n",
    "        df.index.name=None\n",
    "        #排序\n",
    "        df.sort_index()\n",
    "        #市值按照亿换算\n",
    "        df.NegotiableMV=df.NegotiableMV/100000000.0\n",
    "        #返回数据表\n",
    "        return df\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "\n",
    "# 获取股票简称、证券代码、内部代码、证券市场\n",
    "# InnerCode_list：内部代码列表\n",
    "# 返回数据表\n",
    "def get_stock_info(InnerCode_list):\n",
    "    df=pd.DataFrame()\n",
    "    #因jy每次最多返回3000条数据，所以要多次查询\n",
    "    #偏移值\n",
    "    offset=0    \n",
    "    while True:\n",
    "        q=query(\n",
    "            #内部代码\n",
    "            jy.SecuMain.InnerCode,\n",
    "            #证券代码\n",
    "            jy.SecuMain.SecuCode,\n",
    "            #证券简称\n",
    "            jy.SecuMain.SecuAbbr,\n",
    "            #证券市场\n",
    "            jy.SecuMain.SecuMarket,\n",
    "        ).filter(\n",
    "            #限定为股票查询\n",
    "            jy.SecuMain.SecuCategory==1,\n",
    "            #按照聚源内部代码查询\n",
    "            jy.SecuMain.InnerCode.in_(InnerCode_list)\n",
    "        ).offset(\n",
    "            #偏移  \n",
    "            offset\n",
    "        )\n",
    "        temp_df=jy.run_query(q)  \n",
    "        if len(temp_df)==0:\n",
    "            break\n",
    "        #追加数据\n",
    "        df=df.append(temp_df)\n",
    "        #偏移值每次递增3000    \n",
    "        offset+=3000  \n",
    "    #按照内部代码索引\n",
    "    df.index=df['InnerCode']\n",
    "    del df['InnerCode']\n",
    "    df.index.name=None\n",
    "    #排序\n",
    "    df.sort_index()\n",
    "    #证券市场\n",
    "    for i in range(len(df)):        \n",
    "        df['SecuMarket'].iloc[i]=SecuMarket_const[df['SecuMarket'].iloc[i]][0:2]\n",
    "    #返回数据表\n",
    "    return df\n",
    "\n",
    "\n",
    "# 获取股票所属行业\n",
    "# InnerCode_list：内部代码列表\n",
    "# 返回数据表\n",
    "def get_stock_industry(InnerCode_list):\n",
    "    #因所属行业随时间改动，此表包括了所有时间的改动信息，所以返回的数据超过3000行\n",
    "    #先找到最近的改动日期，再查询\n",
    "    q=query(\n",
    "        jy.LC_CSIIndustry.EndDate\n",
    "    ).filter(\n",
    "        jy.LC_CSIIndustry.InnerCode==InnerCode_list[0],\n",
    "    )\n",
    "    df=jy.run_query(q)    \n",
    "    #找到最近改动日期\n",
    "    EndDate=max(df['EndDate'])\n",
    "    df=pd.DataFrame()\n",
    "    #因jy每次最多返回3000条数据，所以要多次查询\n",
    "    #偏移值\n",
    "    offset=0    \n",
    "    while True:\n",
    "        q=query(\n",
    "            #内部代码\n",
    "            jy.LC_CSIIndustry.InnerCode,\n",
    "            #行业\n",
    "            jy.LC_CSIIndustry.FstIndNameCSI,\n",
    "        ).filter(\n",
    "            #按照内部代码查询\n",
    "            jy.LC_CSIIndustry.InnerCode.in_(InnerCode_list),\n",
    "            #按照最后更改日期查找\n",
    "            jy.LC_CSIIndustry.EndDate==EndDate\n",
    "        ).offset(\n",
    "            #偏移  \n",
    "            offset\n",
    "        )\n",
    "        temp_df=jy.run_query(q)  \n",
    "        if len(temp_df)==0:\n",
    "            break\n",
    "        #追加数据\n",
    "        df=df.append(temp_df)\n",
    "        #偏移值每次递增3000    \n",
    "        offset+=3000  \n",
    "    #按照内部代码索引\n",
    "    df.index=df['InnerCode']\n",
    "    del df['InnerCode']\n",
    "    df.index.name=None\n",
    "    #排序\n",
    "    df.sort_index()\n",
    "    #返回数据表\n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "#==================================================================================================================\n",
    "# 指数信息\n",
    "#------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "# 获取指数流通市值\n",
    "def get_index_cmc(index_code):\n",
    "    # 截至最近一个交易日\n",
    "    end_date=pd.datetime.today()   \n",
    "    # 获取当前所有成份股   \n",
    "    stocks=get_index_stocks(index_code,end_date)\n",
    "    q = query(\n",
    "        valuation.market_cap,\n",
    "        valuation.circulating_market_cap,\n",
    "    ).filter(\n",
    "        valuation.code.in_(stocks)\n",
    "    )       \n",
    "    df=get_fundamentals(q,end_date)\n",
    "    # 单位：亿\n",
    "    return round(df.circulating_market_cap.sum(),2)\n",
    "\n",
    "\n",
    "# 获取指数基本信息\n",
    "# code：证券代码\n",
    "def get_index_info(code):\n",
    "    q=query(\n",
    "        #内部代码\n",
    "        jy.LC_IndexBasicInfo.IndexCode,\n",
    "        #类别\n",
    "        jy.LC_IndexBasicInfo.IndexType,\n",
    "        #编制机构\n",
    "        jy.LC_IndexBasicInfo.CreatIndexOrgName,\n",
    "        #权重方式\n",
    "        jy.LC_IndexBasicInfo.WAMethod,\n",
    "        #发布日期\n",
    "        jy.LC_IndexBasicInfo.PubDate,\n",
    "        #基准日期\n",
    "        jy.LC_IndexBasicInfo.BaseDate,\n",
    "        #基准点位\n",
    "        jy.LC_IndexBasicInfo.BasePoint,\n",
    "        #成份股数量\n",
    "        jy.LC_IndexBasicInfo.ComponentSum,\n",
    "        #调整日期\n",
    "        jy.LC_IndexBasicInfo.ComponentAdPeriod,\n",
    "        #简要说明\n",
    "        jy.LC_IndexBasicInfo.IndexAbstract,   \n",
    "    ).filter(\n",
    "        #按照内部代码查询\n",
    "        jy.LC_IndexBasicInfo.IndexCode==code\n",
    "    )\n",
    "    #查询LC_IndexBasicInfo表\n",
    "    df=jy.run_query(q)\n",
    "    #返回数据表\n",
    "    return df\n",
    "\n",
    "\n",
    "# 获取指数成份股及权重\n",
    "# code：聚源内部代码\n",
    "def get_index_stock(code):\n",
    "    # 查询LC_IndexComponentsWeight表\n",
    "    # 先获取最后更改日期\n",
    "    q=query(\n",
    "        jy.LC_IndexComponentsWeight.EndDate\n",
    "    ).filter(\n",
    "        jy.LC_IndexComponentsWeight.IndexCode==code,\n",
    "    )\n",
    "    df=jy.run_query(q)\n",
    "    # 最后更改日期\n",
    "    EndDate=max(df['EndDate'])\n",
    "    # 按照最后更改日期查询\n",
    "    df=pd.DataFrame()\n",
    "    #因jy每次最多返回3000条数据，所以要多次查询\n",
    "    #偏移值\n",
    "    offset=0    \n",
    "    while True:\n",
    "        q=query(\n",
    "            #内部代码\n",
    "            jy.LC_IndexComponentsWeight.InnerCode,\n",
    "            #权重\n",
    "            jy.LC_IndexComponentsWeight.Weight,\n",
    "        ).filter(\n",
    "            #按照内部代码及最后日期查询\n",
    "            jy.LC_IndexComponentsWeight.IndexCode==code,\n",
    "            jy.LC_IndexComponentsWeight.EndDate==EndDate\n",
    "        ).offset(\n",
    "            #偏移  \n",
    "            offset\n",
    "        )\n",
    "        temp_df=jy.run_query(q)  \n",
    "        if len(temp_df)==0:\n",
    "            break\n",
    "        #追加数据\n",
    "        df=df.append(temp_df)\n",
    "        #偏移值每次递增3000    \n",
    "        offset+=3000    \n",
    "    df.index=df['InnerCode']\n",
    "    del df['InnerCode']\n",
    "    df.index.name=None\n",
    "    #排序\n",
    "    df.sort_index()\n",
    "    return df\n",
    "\n",
    "\n",
    "# 获取指数行情  \n",
    "# code：聚源内部代码\n",
    "# count：天数\n",
    "def get_index_market(code,count=5):\n",
    "    q=query(\n",
    "        #交易日\n",
    "        jy.QT_IndexQuote.TradingDay,\n",
    "        #收盘价\n",
    "        jy.QT_IndexQuote.ClosePrice,\n",
    "        #涨幅\n",
    "        jy.QT_IndexQuote.ChangePCT,\n",
    "        #开盘价\n",
    "        jy.QT_IndexQuote.OpenPrice,\n",
    "        #最高价\n",
    "        jy.QT_IndexQuote.HighPrice,\n",
    "        #最低价\n",
    "        jy.QT_IndexQuote.LowPrice,\n",
    "        #昨日收盘价\n",
    "        jy.QT_IndexQuote.PrevClosePrice,  \n",
    "        #成交量\n",
    "        jy.QT_IndexQuote.TurnoverVolume,\n",
    "        #成交额\n",
    "        jy.QT_IndexQuote.TurnoverValue,\n",
    "        #流通市值\n",
    "        jy.QT_IndexQuote.NegotiableMV,\n",
    "    ).filter(\n",
    "        #按照内部代码查询\n",
    "        jy.QT_IndexQuote.InnerCode==code,\n",
    "    ).order_by(\n",
    "        #按证交易日降序排列\n",
    "        jy.QT_IndexQuote.TradingDay.desc()\n",
    "        #查询最近5个交易日\n",
    "    ).limit(count)\n",
    "    #查询QT_IndexQuote表\n",
    "    df=jy.run_query(q)\n",
    "    # 按照交易日索引\n",
    "    df.index=df.TradingDay\n",
    "    df.index.name=None\n",
    "    del df['TradingDay']\n",
    "    #返回数据表\n",
    "    return df\n",
    "\n",
    "\n",
    "# 获取指数财务指标  \n",
    "# code：聚宽代码\n",
    "def get_index_finance(code):\n",
    "    date_list=['2018-03-30','2017-12-30','2017-09-30','2017-06-30','2017-03-30']\n",
    "    quarter_list=['2018q1','2017q4','2017q3','2017q2','2017q1']\n",
    "    data_list=[]\n",
    "    #code=Code.secu_to_jq(code)\n",
    "    #获取近五年财务信息\n",
    "    for quarter,date in zip(quarter_list,date_list):\n",
    "        stocks=get_index_stocks(code,date)\n",
    "        q=query(\n",
    "            income.code,\n",
    "            income.total_operating_revenue,\n",
    "            income.np_parent_company_owners,\n",
    "            balance.total_assets,\n",
    "            balance.total_liability\n",
    "        ).filter(\n",
    "            income.code.in_(stocks)\n",
    "        )       \n",
    "        df=get_fundamentals(q,statDate=quarter)\n",
    "        yysr=round(sum(df.total_operating_revenue)/100000000.0,3)\n",
    "        jlr=round(sum(df.np_parent_company_owners)/100000000.0,3)\n",
    "        jzc=round((sum(df.total_assets)-sum(df.total_liability))/100000000.0,3)\n",
    "        \n",
    "        data_list.append([yysr,jlr,jzc])\n",
    "\n",
    "    df=pd.DataFrame(index=quarter_list,data=data_list,columns=['营业收入','净利润','净资产'])\n",
    "    return df.T\n",
    "\n",
    "\n",
    "# 显示指数成份股  \n",
    "# code：聚源内部代码\n",
    "def show_index_stocks(code):\n",
    "    df=pd.DataFrame()\n",
    "    # 获取指数成份股内部代码列表\n",
    "    df=get_index_stock(code)\n",
    "    #按照内部代码拼接数据\n",
    "    #基本信息\n",
    "    info_df=get_stock_info(df.index)\n",
    "    #行业信息\n",
    "    industry_df=get_stock_industry(df.index)\n",
    "    #财务信息\n",
    "    valuation_df=get_stock_valuation(df.index)\n",
    "    #拼接数据\n",
    "    df=pd.concat([info_df,industry_df,valuation_df,df],axis=1)  \n",
    "    #整理表                             \n",
    "    df.index=df['SecuCode']  \n",
    "    df.index.name=None\n",
    "    del df['SecuCode'] \n",
    "    #按照权重降序排序\n",
    "    df=df.sort(['Weight'],ascending=False)\n",
    "    #更改标题\n",
    "    df=df.rename(columns={'SecuAbbr':u'名称','SecuMarket':u'证券交易所',\n",
    "                          'NegotiableMV':u'流通市值(亿)',\n",
    "                          'Weight':u'权重%','FstIndNameCSI':u'所属行业'})\n",
    "    return df\n",
    "\n",
    "\n",
    "# 显示指数行情  \n",
    "# code：聚源内部代码\n",
    "def show_index_market(code):\n",
    "    df=get_index_market(code)\n",
    "    #成交量换算为万手\n",
    "    df.TurnoverVolume=np.round(df.TurnoverVolume/1000000.0,3)\n",
    "    #成交额、流通市值换算为亿\n",
    "    df.TurnoverValue=np.round(df.TurnoverValue/100000000.0,3)\n",
    "    df.NegotiableMV=df.NegotiableMV/100000000.0\n",
    "    df=df.rename(columns={'TradingDay':'日期','PrevClosePrice':'昨收','OpenPrice':'开盘','HighPrice':'最高',\n",
    "                          'LowPrice':'最低','ClosePrice':'今收','TurnoverVolume':'成交量(万手)','TurnoverValue':'成交额(亿元)',\n",
    "                          'ChangePCT':'涨跌幅(%)','NegotiableMV':'流通市值(亿元)'\n",
    "                         })\n",
    "    return df\n",
    "\n",
    "\n",
    "# 显示指数基本信息  \n",
    "# code：聚源内部代码\n",
    "def show_index_info(code):  \n",
    "    # 字段名\n",
    "    col_label=['说明']\n",
    "    # 项目\n",
    "    row_labels=['指数代码','指数名称','指数类别','编制机构','权重方法','发布日期','基准日期','基准点位','成份股数','调整周期',\n",
    "                '简要说明','流通市值']\n",
    "    info_df=pd.DataFrame(index=row_labels,columns=col_label)\n",
    "    #获取基本信息\n",
    "    df=get_index_info(code)\n",
    "    #证券代码\n",
    "    info_df.iloc[0,0]=index_code\n",
    "    #中文名称\n",
    "    info_df.iloc[1,0]=index_name\n",
    "    #证券类别   \n",
    "    info_df.iloc[2,0]=IndexType_const[int(df['IndexType'].iloc[0])] if not math.isnan(df['IndexType'].iloc[0]) else ''\n",
    "    #编制机构   \n",
    "    info_df.iloc[3,0]=df['CreatIndexOrgName'].iloc[0]\n",
    "    #权重方法\n",
    "    info_df.iloc[4,0]=WAMethod_const[int(df['WAMethod'].iloc[0])] if not math.isnan(df['WAMethod'].iloc[0]) else ''\n",
    "    #发布日期  \n",
    "    info_df.iloc[5,0]=str(df['PubDate'].iloc[0])[0:10]\n",
    "    #基日\n",
    "    info_df.iloc[6,0]=str(df['BaseDate'].iloc[0])[0:10]\n",
    "    #基点\n",
    "    info_df.iloc[7,0]=df['BasePoint'].iloc[0]\n",
    "    #成份股数量   \n",
    "    info_df.iloc[8,0]=df['ComponentSum'].iloc[0]\n",
    "    #调整周期   \n",
    "    info_df.iloc[9,0]=ComponentAdPeriod_const[int(df['ComponentAdPeriod'].iloc[0])] if not math.isnan(df['ComponentAdPeriod'].iloc[0]) else ''\n",
    "    #简要说明\n",
    "    info_df.iloc[10,0]=df['IndexAbstract'].iloc[0]\n",
    "    #流通市值\n",
    "    info_df.iloc[11,0]='%.2f亿'%(get_index_cmc(jq_code))\n",
    "    #返回数据表\n",
    "    return info_df\n",
    "\n",
    "\n",
    "# 显示指数PE表\n",
    "# code：聚宽代码\n",
    "def show_index_pe(code):\n",
    "    df=index.value.read(10)\n",
    "#     df.index=df.index.astype('str').str[0:6] \n",
    "    df=df[df.index == code]\n",
    "    #去除代码后缀\n",
    "\n",
    "    df=df[['name','pe_e','pe_e_ratio','pe_e_state',\n",
    "           'pe_e_min','pe_e_max','pe_e_median','pe_e_drop',\n",
    "           'pe_e_q10','pe_e_q20','pe_e_q40','pe_e_q60','pe_e_q80','pe_e_q90']]\n",
    "    df=df.rename(columns={'name':'名称','pe_e':'PE(等权)','pe_e_ratio':'高度','pe_e_state':'区间',\n",
    "                'pe_e_min':'最低值','pe_e_max':'最高值','pe_e_median':'中位值','pe_e_drop':'距底',\n",
    "                'pe_e_q10':'10%值','pe_e_q20':'20%值','pe_e_q40':'40%值','pe_e_q60':'60%值',\n",
    "                'pe_e_q80':'80%值','pe_e_q90':'90%值',}) \n",
    "    return df \n",
    "\n",
    "\n",
    "# 显示指数PB表\n",
    "# code：聚宽代码\n",
    "def show_index_pb(code):\n",
    "    df=index.value.read(10)\n",
    "    df=df[df.index == code]\n",
    "    df=df[['name','pb_e','pb_e_ratio','pb_e_state',\n",
    "           'pb_e_min','pb_e_max','pb_e_median','pb_e_drop',\n",
    "           'pe_e_q10','pe_e_q20','pe_e_q40','pe_e_q60','pe_e_q80','pe_e_q90']]\n",
    "    df=df.rename(columns={'name':'名称','pb_e':'PB(等权)','pb_e_ratio':'高度','pb_e_state':'区间',\n",
    "                'pb_e_min':'最低值','pb_e_max':'最高值','pb_e_median':'中位值','pb_e_drop':'距底',\n",
    "                'pbs10':'10%值','pbs20':'20%值','pbs40':'40%值','pbs60':'60%值',\n",
    "                'pe_e_q80':'80%值','pe_e_q90':'90%值',}) \n",
    "    return df \n",
    "\n",
    "\n",
    "#指数成份构成饼图\n",
    "def pie_index_stock(df):   \n",
    "    df=df.groupby(u'所属行业').sum()\n",
    "    df=df[[u'权重%']]\n",
    "    fig,ax=plt.subplots(figsize=(8,8))\n",
    "    df.plot(kind='pie',ax=ax,subplots=True,fontsize=12)\n",
    "    ax.set_title('指数构成（行业权重）',fontsize=14,alpha=0.9) \n",
    "    ax.set_ylabel('')  \n",
    "\n",
    "\n",
    "#==================================================================================================================\n",
    "# 基金信息\n",
    "#------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "#获取基金行情\n",
    "def get_fund_price(codes):\n",
    "    fund_list=Code.secu_to_jq(list(codes))\n",
    "    #最近一个交易日\n",
    "    end_date=pd.datetime.today()-timedelta(1)\n",
    "    df=pd.DataFrame()\n",
    "    #获取行情信息\n",
    "    for code in fund_list:\n",
    "        price_df=get_price(code,end_date=end_date,count=1,frequency='daily', \n",
    "                      fields=['close','low','high','open','pre_close','volume','money']) \n",
    "        df=pd.concat([df,price_df],axis=0)    \n",
    "    #成交额、成交量换算单位\n",
    "    df.volume=np.round(df.volume/1000000.0,3)\n",
    "    df.money=np.round(df.money/10000.0,3)\n",
    "    #计算涨跌、涨幅\n",
    "    df['value']=np.round(df.close-df.pre_close,3)\n",
    "    df['change']=np.round(df.value/df.close*100,3)    \n",
    "    #插入最近交易日列\n",
    "    df.insert(0,'trade-date',df.index.tolist())\n",
    "    #按照代码索引\n",
    "    df.index=codes\n",
    "    #删除多余列\n",
    "    del df['pre_close']\n",
    "    #去除索引名\n",
    "    df.index.name=None  \n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "# 获取基金净值\n",
    "def get_fund_extras(codes):\n",
    "    fund_list=Code.secu_to_jq(list(codes))\n",
    "    #最近一个交易日\n",
    "    end_date=pd.datetime.today()#-timedelta(1)\n",
    "    #获取单位净值\n",
    "    unit_df=get_extras('unit_net_value',fund_list,end_date=end_date,count=1,df=True)\n",
    "    unit_df=unit_df.T\n",
    "    unit_df.columns=['unit_net_value']\n",
    "    unit_df.index=codes\n",
    "    #获取累计净值\n",
    "    acc_df=get_extras('acc_net_value',fund_list,end_date=end_date,count=1,df=True)\n",
    "    acc_df=acc_df.T\n",
    "    acc_df.columns=['acc_net_value']\n",
    "    acc_df.index=codes\n",
    "    #返回数据表\n",
    "    return unit_df,acc_df\n",
    "\n",
    "\n",
    "# 获取指数对应的基金\n",
    "# code：聚宽代码\n",
    "def get_index_fund(code):\n",
    "    #读取基金表\n",
    "    df=fund.pool.show()\n",
    "    #查询当前指数所有基金\n",
    "    df=df[df['index_code']==code]\n",
    "    if len(df)==0:\n",
    "        return None\n",
    "    else: \n",
    "        #重新索引\n",
    "#         df.index=df.fund_code\n",
    "#         del df['fund_code']\n",
    "#         df.index.name=None\n",
    "        #重新排序\n",
    "        df.sort_index()\n",
    "        #删除多余的列\n",
    "        del df['index_name']\n",
    "        del df['index_code']\n",
    "        return df\n",
    "    \n",
    "\n",
    "#显示指数对应的基金\n",
    "# code：聚宽代码\n",
    "def show_index_fund(code):\n",
    "    #获取指数关联基金\n",
    "    df=get_index_fund(code)\n",
    "    if len(df)==0:\n",
    "        print '无对应基金！'\n",
    "        return\n",
    "\n",
    "    #获取基金行情\n",
    "    price_df=get_fund_price(df.index) \n",
    "    #获取基金净值\n",
    "    unit_df,acc_df=get_fund_extras(df.index) \n",
    "    #组织数据\n",
    "    df=pd.concat([df,price_df,unit_df,acc_df],axis=1)  \n",
    "    #去除代码后缀\n",
    "    #df.index=df.index.str[0:6]\n",
    "    #重命名标题\n",
    "    df=df.rename(columns={\n",
    "            'name':'基金名称','start_date':'成立日期','type':'类别',\n",
    "            #'pre_close':'昨收',\n",
    "            'open':'开盘','high':'最高',\n",
    "            'low':'最低','close':'收盘','volume':'成交量(万)','money':'成交额(万)',\n",
    "            'change':'涨跌幅(%)','value':'涨跌额',\n",
    "            'trade-date':'最近交易日','unit_net_value':'单位净值','acc_net_value':'累计净值'\n",
    "            })\n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "#显示当前指数提示\n",
    "def show_index_title(title):\n",
    "    print '指数：%s %s %s'%(index_code,index_name,title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 当前指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 \n"
     ]
    }
   ],
   "source": [
    "#证券代码\n",
    "index_code='000300.SH'\n",
    "#聚宽代码\n",
    "jq_code=Code.secu_to_jq(index_code)\n",
    "#聚源内部代码\n",
    "jy_code=Code.idx_to_jy(index_code)\n",
    "#指数名称\n",
    "index_name=index.pool.name(index_code)\n",
    "\n",
    "show_index_title('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.基本信息\n",
    "- 指数发布日期、编制机构、编制方法等信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 基本信息一览\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>说明</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>指数代码</th>\n",
       "      <td>000300.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>指数名称</th>\n",
       "      <td>沪深300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>指数类别</th>\n",
       "      <td>规模类指数</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>编制机构</th>\n",
       "      <td>中证指数有限公司</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>权重方法</th>\n",
       "      <td>流通股比例分级靠档加权</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>发布日期</th>\n",
       "      <td>2005-04-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基准日期</th>\n",
       "      <td>2004-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基准点位</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成份股数</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>调整周期</th>\n",
       "      <td>半年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>简要说明</th>\n",
       "      <td>沪深300指数由上海和深圳证券市场中市值大、流动性好的300只股票组成，综合反映中国A股市场上市股票价格的整体表现。“沪深300指数”商标归属于中证指数有限公司，未经中证指数有限公司事先书面同...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>流通市值</th>\n",
       "      <td>233654.45亿</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                       说明\n",
       "指数代码                                                                                            000300.SH\n",
       "指数名称                                                                                                沪深300\n",
       "指数类别                                                                                                规模类指数\n",
       "编制机构                                                                                             中证指数有限公司\n",
       "权重方法                                                                                          流通股比例分级靠档加权\n",
       "发布日期                                                                                           2005-04-08\n",
       "基准日期                                                                                           2004-12-31\n",
       "基准点位                                                                                                 1000\n",
       "成份股数                                                                                                  300\n",
       "调整周期                                                                                                   半年\n",
       "简要说明  沪深300指数由上海和深圳证券市场中市值大、流动性好的300只股票组成，综合反映中国A股市场上市股票价格的整体表现。“沪深300指数”商标归属于中证指数有限公司，未经中证指数有限公司事先书面同...\n",
       "流通市值                                                                                           233654.45亿"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#指数基本信息\n",
    "show_index_title('基本信息一览')\n",
    "pd.set_option('max_colwidth', 100)\n",
    "df=show_index_info(jy_code)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.PE\n",
    "- 指数PE最新值、百分位，各重要百分位点，PE折线图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 PE估值、走势图、频谱图\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>名称</th>\n",
       "      <th>PE(等权)</th>\n",
       "      <th>高度</th>\n",
       "      <th>区间</th>\n",
       "      <th>最低值</th>\n",
       "      <th>最高值</th>\n",
       "      <th>中位值</th>\n",
       "      <th>距底</th>\n",
       "      <th>10%值</th>\n",
       "      <th>20%值</th>\n",
       "      <th>40%值</th>\n",
       "      <th>60%值</th>\n",
       "      <th>80%值</th>\n",
       "      <th>90%值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000300.SH</th>\n",
       "      <td>沪深300</td>\n",
       "      <td>15.16</td>\n",
       "      <td>5.43</td>\n",
       "      <td>极低</td>\n",
       "      <td>11.69</td>\n",
       "      <td>44.42</td>\n",
       "      <td>20.92</td>\n",
       "      <td>-22.89</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.78</td>\n",
       "      <td>19.33</td>\n",
       "      <td>22.34</td>\n",
       "      <td>24.47</td>\n",
       "      <td>33.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              名称  PE(等权)    高度  区间    最低值    最高值    中位值     距底   10%值   20%值  \\\n",
       "000300.SH  沪深300   15.16  5.43  极低  11.69  44.42  20.92 -22.89  15.69  16.78   \n",
       "\n",
       "            40%值   60%值   80%值   90%值  \n",
       "000300.SH  19.33  22.34  24.47  33.46  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FMrMdzrnnIlpUzAK6OOfizWyXH8BoCEw0s5/9daYCNzrnUs1std9N6gbgR2AD0DnK7s7A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\nPp6ePXvy9ttvc9FFF4Utj3V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AgOSiIgQZpbhY6tAh8uDSeFpjFiyQJkyQ9tsv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gTIHp07h78eONBO0mDD9AoKLBgK2ht2OkyYYH927rpLKimJvhPDmDHh8u5TT6U9BsGzeDFb52aT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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2172c6e7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EkiRJkiQVmuGIJEmSJEkqNMMRSZIkSZJUaIYjkiRJkiSp0AxHJEmSJElSoRmO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f215f0b52d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#指数PE信息（PE分位值、走势图、频谱图）\n",
    "show_index_title('PE估值、走势图、频谱图')\n",
    "index.value.chart.line_one(index_code,item='pe',mode='e')\n",
    "index.value.chart.hist(index_code,item='pe',mode='e')\n",
    "df=show_index_pe(index_code)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.PB\n",
    "- 指数PB最新值、百分位，各重要百分位点，PE折线图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 PB估值、走势图、频谱图\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>名称</th>\n",
       "      <th>PB(等权)</th>\n",
       "      <th>高度</th>\n",
       "      <th>区间</th>\n",
       "      <th>最低值</th>\n",
       "      <th>最高值</th>\n",
       "      <th>中位值</th>\n",
       "      <th>距底</th>\n",
       "      <th>pe_e_q10</th>\n",
       "      <th>pe_e_q20</th>\n",
       "      <th>pe_e_q40</th>\n",
       "      <th>pe_e_q60</th>\n",
       "      <th>80%值</th>\n",
       "      <th>90%值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000300.SH</th>\n",
       "      <td>沪深300</td>\n",
       "      <td>1.68</td>\n",
       "      <td>8.34</td>\n",
       "      <td>极低</td>\n",
       "      <td>1.51</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.22</td>\n",
       "      <td>-10.12</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.78</td>\n",
       "      <td>19.33</td>\n",
       "      <td>22.34</td>\n",
       "      <td>24.47</td>\n",
       "      <td>33.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              名称  PB(等权)    高度  区间   最低值   最高值   中位值     距底  pe_e_q10  \\\n",
       "000300.SH  沪深300    1.68  8.34  极低  1.51  4.07  2.22 -10.12     15.69   \n",
       "\n",
       "           pe_e_q20  pe_e_q40  pe_e_q60   80%值   90%值  \n",
       "000300.SH     16.78     19.33     22.34  24.47  33.46  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EREREJBd5tQgpjHdzN8bMNgI45ybhtbBIB+YDU80syzn3JVDCzPaFbeOfYdNNgd/wbsZb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\nff/jj1iPyHkffcQuLpMmhfa4v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f215c264710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5vAdck1J6u+z6/42ITmShUBeyZYD/lte+kSRJkiSp3UTzB2pUnBhxANCLrKjm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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f215f17e350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#指数PB信息（PB分位值、走势图、频谱图）\n",
    "show_index_title('PB估值、走势图、频谱图')\n",
    "index.value.chart.line_one(index_code,item='pb',mode='e')\n",
    "index.value.chart.hist(index_code,item='pb',mode='e')\n",
    "df=show_index_pb(index_code)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.财务信息\n",
    "- 指数最近四个季度的营收、利润、净资产。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 财务信息，ROE、股息率走势\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2018q1</th>\n",
       "      <th>2017q4</th>\n",
       "      <th>2017q3</th>\n",
       "      <th>2017q2</th>\n",
       "      <th>2017q1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>营业收入</th>\n",
       "      <td>64503.077</td>\n",
       "      <td>70104.833</td>\n",
       "      <td>60113.298</td>\n",
       "      <td>59575.107</td>\n",
       "      <td>58051.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净利润</th>\n",
       "      <td>7156.322</td>\n",
       "      <td>5738.446</td>\n",
       "      <td>6598.764</td>\n",
       "      <td>6718.365</td>\n",
       "      <td>6248.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净资产</th>\n",
       "      <td>253972.599</td>\n",
       "      <td>247509.643</td>\n",
       "      <td>234805.959</td>\n",
       "      <td>226848.522</td>\n",
       "      <td>223225.623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2018q1      2017q4      2017q3      2017q2      2017q1\n",
       "营业收入   64503.077   70104.833   60113.298   59575.107   58051.779\n",
       "净利润     7156.322    5738.446    6598.764    6718.365    6248.585\n",
       "净资产   253972.599  247509.643  234805.959  226848.522  223225.623"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1yapJMIPOqrjZeCI9VCI9ZwoTDMfpQNCDZA/wHkHOkk4Es65camavE7x/aeF2J4frnWC6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\n3IQIhDiOc3A196mHvIiIxEyiBULq1oU99rBxurGUnQ1PPgkPPgg7d1a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//cuCJddeaz1G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+xq6QMPwK23hnYM31KPeFKxd4lnmkSsDR1qozs/+wy++UaBEImcZAmCgAUy9tvPfm+qUrE0Bmxk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f215c6862d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nnRQ8h7Vq1coxf07nFGDx4sUkJCQwevToHPOPGzeO+vXrU61aNZKTk9m6dSug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\n+3ZuvPHGiMGrRYsWRQz8hTI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ztm3bBod9jgZXXHFFcHjupKSkbN8Rhw8ftgsuuMCmTp2a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i5axfLbDS5HldX2rfS0f6aVTSnl+VLKL6nOqNLUTXcdeUmStYGdgdtb6Wl/NJJJfRrt+4F3UBVD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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f215ef67510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_index_title('财务信息，ROE、股息率走势')\n",
    "#指数ROE、股息率走势\n",
    "index.value.chart.line_one(index_code,item='dyr',mode='')\n",
    "index.value.chart.line_one(index_code,item='roe',mode='')\n",
    "#指数财务信息\n",
    "df=get_index_finance(jq_code)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.行情信息\n",
    "- 指数最新收盘、开盘价、成交额等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 行情信息\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>今收</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>开盘</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>昨收</th>\n",
       "      <th>成交量(万手)</th>\n",
       "      <th>成交额(亿元)</th>\n",
       "      <th>流通市值(亿元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>3334.5036</td>\n",
       "      <td>-0.495080</td>\n",
       "      <td>3333.3801</td>\n",
       "      <td>3356.5757</td>\n",
       "      <td>3310.8726</td>\n",
       "      <td>3351.0942</td>\n",
       "      <td>7620.563</td>\n",
       "      <td>960.919</td>\n",
       "      <td>101451.1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-30</th>\n",
       "      <td>3351.0942</td>\n",
       "      <td>-1.047649</td>\n",
       "      <td>3385.8052</td>\n",
       "      <td>3402.5626</td>\n",
       "      <td>3349.4688</td>\n",
       "      <td>3386.5736</td>\n",
       "      <td>6838.529</td>\n",
       "      <td>802.261</td>\n",
       "      <td>101955.8805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-29</th>\n",
       "      <td>3386.5736</td>\n",
       "      <td>-0.399889</td>\n",
       "      <td>3393.0527</td>\n",
       "      <td>3398.7139</td>\n",
       "      <td>3377.1231</td>\n",
       "      <td>3400.1705</td>\n",
       "      <td>6702.040</td>\n",
       "      <td>790.723</td>\n",
       "      <td>103035.3287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28</th>\n",
       "      <td>3400.1705</td>\n",
       "      <td>-0.187960</td>\n",
       "      <td>3408.1502</td>\n",
       "      <td>3416.5929</td>\n",
       "      <td>3388.8143</td>\n",
       "      <td>3406.5735</td>\n",
       "      <td>7544.536</td>\n",
       "      <td>949.459</td>\n",
       "      <td>103449.0091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>3406.5735</td>\n",
       "      <td>2.443026</td>\n",
       "      <td>3339.3894</td>\n",
       "      <td>3406.5735</td>\n",
       "      <td>3339.2646</td>\n",
       "      <td>3325.3347</td>\n",
       "      <td>9427.008</td>\n",
       "      <td>1149.610</td>\n",
       "      <td>103643.8184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   今收    涨跌幅(%)         开盘         最高         最低         昨收  \\\n",
       "2018-08-31  3334.5036 -0.495080  3333.3801  3356.5757  3310.8726  3351.0942   \n",
       "2018-08-30  3351.0942 -1.047649  3385.8052  3402.5626  3349.4688  3386.5736   \n",
       "2018-08-29  3386.5736 -0.399889  3393.0527  3398.7139  3377.1231  3400.1705   \n",
       "2018-08-28  3400.1705 -0.187960  3408.1502  3416.5929  3388.8143  3406.5735   \n",
       "2018-08-27  3406.5735  2.443026  3339.3894  3406.5735  3339.2646  3325.3347   \n",
       "\n",
       "             成交量(万手)   成交额(亿元)     流通市值(亿元)  \n",
       "2018-08-31  7620.563   960.919  101451.1183  \n",
       "2018-08-30  6838.529   802.261  101955.8805  \n",
       "2018-08-29  6702.040   790.723  103035.3287  \n",
       "2018-08-28  7544.536   949.459  103449.0091  \n",
       "2018-08-27  9427.008  1149.610  103643.8184  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取指数行情信息（价格走势图）\n",
    "show_index_title('行情信息')\n",
    "df=show_index_market(jy_code)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.指数构成及成份股信息\n",
    "- 指数十大成份股信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 十大成份股、行业构成\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/pandas/core/indexing.py:115: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>名称</th>\n",
       "      <th>证券交易所</th>\n",
       "      <th>所属行业</th>\n",
       "      <th>权重%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>601318</th>\n",
       "      <td>中国平安</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融地产</td>\n",
       "      <td>6.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600519</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>上海</td>\n",
       "      <td>主要消费</td>\n",
       "      <td>3.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600036</th>\n",
       "      <td>招商银行</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融地产</td>\n",
       "      <td>2.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000333</th>\n",
       "      <td>美的集团</td>\n",
       "      <td>深圳</td>\n",
       "      <td>可选消费</td>\n",
       "      <td>1.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601166</th>\n",
       "      <td>兴业银行</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融地产</td>\n",
       "      <td>1.853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000651</th>\n",
       "      <td>格力电器</td>\n",
       "      <td>深圳</td>\n",
       "      <td>可选消费</td>\n",
       "      <td>1.848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600016</th>\n",
       "      <td>民生银行</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融地产</td>\n",
       "      <td>1.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601328</th>\n",
       "      <td>交通银行</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融地产</td>\n",
       "      <td>1.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600887</th>\n",
       "      <td>伊利股份</td>\n",
       "      <td>上海</td>\n",
       "      <td>主要消费</td>\n",
       "      <td>1.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600276</th>\n",
       "      <td>恒瑞医药</td>\n",
       "      <td>上海</td>\n",
       "      <td>医药卫生</td>\n",
       "      <td>1.441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          名称 证券交易所  所属行业    权重%\n",
       "601318  中国平安    上海  金融地产  6.725\n",
       "600519  贵州茅台    上海  主要消费  3.265\n",
       "600036  招商银行    上海  金融地产  2.876\n",
       "000333  美的集团    深圳  可选消费  1.891\n",
       "601166  兴业银行    上海  金融地产  1.853\n",
       "000651  格力电器    深圳  可选消费  1.848\n",
       "600016  民生银行    上海  金融地产  1.669\n",
       "601328  交通银行    上海  金融地产  1.522\n",
       "600887  伊利股份    上海  主要消费  1.450\n",
       "600276  恒瑞医药    上海  医药卫生  1.441"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I8NFWW7HHHXeQ2nDD5vfv1w922on0ebEzXBfZNvDwSOM+OqZMSe9WWRnqBekz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\n3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TRpEo888gipqaksWbKE119/nWHDhhEcHIyIYIxh3759lC1bNs/vPzc0yKpCxzGX8jfH\n9pkxxuskhEyH1kvh4WRoUgy8m4G9DRS/H7gH/c9wOQIcB7YCm8G2HpJ+BX9/OCnwU5z15WYb8IuI\n2N1YVZXPwsLCqFGjBj4+Ptjt1l99UFCQc45q5lxXX19fBg4cyOjRo53nRkZGOoPuvffee0mL+Gpa\nt25NmTJlmD17Nk2bNgUgPDychQsXYrfbeeWVV/LiFm+YrvikbjqOdZTvAO4vCW2AFqlQ4Q5Irgc+\noeAfDARjtXhvtme7ccAOYCvIOojfDj5pYCsGOy/AGrsVb3eISJybq1po6drF+UMTBCiVT4wxgUAd\nINgfQotBgxS4Mx0CboekulAkFAIyg291PHv+WwzWM9QjwCGQX63Wafof4JMAPiXhfymwLgk2YbVS\nj+unfv7RIJs/NMgq5WbGmNJYcTW4GNT1h4YpUNMGJapDcg0wt4LPLeBXEUxFoAJQ0bEVJ/9bwqlA\nLHABa7EHRyC174ek/0HGcSiaDgTACQOHE2BfmtW1nhl3/9RuX/fSIJs/NMgqVUAZY0piBd9qQIUi\nUDkAqhWBKgKVbFA2BUoJeAVCallIrwzcAj6VwM8XvHzAeGMlsvfGeibsfdGWdV8yVuCMAXs0pEZD\n+nmwxwCxYOKhSCL4ZljzUJN8IKEI/CXwWyzstVtBNDOQRuuneMGlQTZ/aJBVysMZYwKwGrUVsvxZ\nDit+FvGCIt7Wnz7eUMSAjxf4GOtnby/wwfq5iB0SUuBsCkRjxdvLbck30ye0MSYIqCoiP13neeuB\n/teS6N0YMx1YLCJLr3JcOSDWMb0s1zTI5g/NwqOUhxORROCoY1N5zBhzDzAP8DXGDBKRNVneqwYs\nJXt+uDkiMsbxs5BDb74x5oSI3Or4uQvwq+PYa4l6s7EyS32Vi9tRhZAGWaWUxzHG+AIvA48D3bGC\n5XxjzGbgTRE5LSLHjDFewBTH+48Ai4wxQ4EhWIPHI40xCSJSL0vxWYNpZ6yc8Y7LmhCgpoh849gx\nwXFM1nM/NMZ8mHWfiOjU7JuUJw+QVErdZIwxRYwx/bEy4wUDD4jIfhH5BagPnAUOGmNmOVqy/vzd\nWvXFerxdFhgG/BdoB/g5yu5ijIkCKhljjhtjmjrOq+W41nSsgP1yZn1EZIiIBM2zfBAAAAt1SURB\nVAHPAVNFpKrj9UNY+W+DNMDe3DTIKqU8yZ1AF6AbVqvxmDEmyRiTDJwG+mIFxD+wpvz+E0hwbDOx\ngnCORGSxI0CedgTGQKA10BMoBjwDhGMteHWxrUBDR8sZ4HVgyw3d6UWyJjJ31XatbDYbe/bsycvb\nK7S0u1gp5TFE5Fegk+NlPwBjzP8BxUXkzSyHvmmMaY4V7LJOR7ofOMa1zazaj/U8diLwKGDDmpXl\nXC7SGHMHcABrthRYQT/z7RaOrmkBQkXk/LXd5RX864ZLyJOyw8PDSUtLo02bNjRv3pyff/6ZOXPm\n8OWXX7J3716OHTtG+/bt6du3L2FhYfj7+/PUU09RpUoVRIRTp04REBBAyZIlMcYwceJEWrVq5bJb\ncydtySqlPIox5g1jTFTmBowAXsi6zxjzMlACWIzVzdsbeB/IzIjYBQgCBgA5riQvIseBk1l2eWF1\nHV+8/t9eR7fw5baqeRJgC4D09HRefPFF1q9fT5cuXYiMjKRTp05kZGTwww8/0KxZMyIiIsjIyMDX\n15d58+bRoUMHjDF07dqVLl26YIwhJSWFgIAAvLy8aNKkSaENsKBBVinlYURklKNbN9jx52hgnOPn\ne4F6IpK5kr1gBdGBWAtuAPwE/IX1jLYcVhAGwBjjDfgbY8YbY6pcdOn7gDeBOVn23VSrbsbGxhId\nHU2rVq3YuXMnzZs3p0WLFuzfv58WLVqwadMmZsyYQVpaGgsWLKBjx474+fk5zz916hTjx48nNTWV\nQ4cOsWzZMo4dO+bGO3I97S5WSnkcY8zjWK3R7he9NQ1YgDVACeAurHWqq+L4vHNM81ljjGkIfANU\ncpT5FDAW+AFYCZzKUm4E1rPaVx0t6SIishGoAiTl/R0WTGXLlqVq1aosWbIkc44pbdq04f777+ez\nzz5j1KhRZGRkMHXqVPbs2cOWLVt47733aNSokbOMHj164OvrC0BGRgYhISHuup18oS1ZpZQnWgTU\nN8bUwmpNGmNMI6ygNz3Lcf/DCsTfAS/imJ5jjCmLNZipoWM/WAnlq4hIdxFZ6VgNQoAiIvKziJxz\nDGxqjbV6JViBfpfrbrPgOX36NO+//z4HDhxg0qRJnDhxgnr16hEREUHNmjUJDg5m9erVlClThoiI\nCF5//XUA58CqBQsWcOrUKU6dOsXmzZsLRAo/V9KWrFLKYzi6c/9wvCwNbMZqLBjgecf+3x0f6EMc\nrzOD5R5glTFmEtAI2I41COqMMaaGiBzJ4ZIbsdIlfuK4hi9WwoSdxpg6wJNYgfqmISIMGzaMUaNG\nER8fT+3atdm0aRNff/01X375JefOnWPTpk3UqlULm83Gtm3bSEhIICAggPT0dJ555hmKFClCeno6\nRYoU4c4773T3LbmUBlmllMcQkQysAUtX5ViwYq1j1a3+VzhuuYjEXOZ6XwJfXua834DW17IsY2Fi\njGHMmDGEhYWxfv16xo0bB8D+/fvp1KkTmzdvJiwsjLZt2/LDDz+wePFibr/9dqpXr054eDizZ8+m\nSpUqzJ49m+eee45atWq5+Y5cS4OsUqpQEpE0cp7TevFxOQbYazjPBuzOzbm59q98vVqOcmrJZgoP\nD+ebb77By8sLb29v5s+fT/fu3YmIiOCjjz5i3LhxHD16lLCwMABWrVrF66+/TmRk5HXN0/Uk+kxW\nKaU8gIi4fLsWxhjGjh3L9u3bmTRpknMhi+DgYFq1aoWPjw8jRoxg1qxZbNiwgYoVK3LmzBnOnTvH\n/PnzGTVqFOnp6RhjePPNN/Hz82PBggUu/u25j7ZklVJKXRcRoXfv3mzZsoWXXnqJ4OBg9u3bR506\ndbDb7Xh7e1O/fn2GDh3K4cOHeeedd5wLVzz//PMsXryY999/H4D58+fj7+/v5jtyHU11p5RSBUxB\nTnUXFxdH0aJFndNwPJnmk1VKqZtQQQ6yhUl+BFl9JquUUkq5iAZZpZRSykU0yCqllFIuokFWKaWU\nchENskoppZSLaJBVSimlXESDrFJKeYDMlZVcueVGVFQUP/3003Wf17JlS44evbZln/v168fSpUuv\nelx0dDQ2m+266+JKGmSVUspDiAu33Ni3bx/t2rXjqaeeYs2aNdneO3bsGPXq1SM0NNS5jRkzxvl+\nZj7ai916663OnxcvXszBgwev+UtAnz59WLRoUS7vxjV0WUWllFLXJS0tjY8//pi5c+eycOFCRISe\nPXty3333MXLkSCpVqkS1atWw2+0MGDAAEeHbb7+lW7dujB8/ngkTJnD8+HHatWtH8eLF2bNnj7Ps\nrMF02bJleHlZbUER4ZdffuG3336ja9euAAwZMoRly5ZlO3f48OEMHz48277jx4+7+ldyWdqSVUop\ndU3S09OZNm0aISEhHDhwgI0bN1KnTh1CQkLYtWsXFSpUoFatWvTt25djx46RlJTkbK2mpaWRkZHB\n+fPnGTNmDI0bNyYyMpLU1FTAarUGBQVx+vRpqlatytatWwE4ePAgBw4coF+/fgwYMICPP/7YWZ8J\nEyYQFRXFxIkT+cc//sHx48eJiopi9erVNGjQgKioKLcGWNAgq5RS6hodOnSIxYsXs2jRIowxVKtW\nDX9/f4oVK0alSpWYNWsWBw4coHr16pQsWZL33nuP4sWLU7x4cZ588kkqVKhw2bK7dOlCVFQUlSpV\n4vjx48TExLB27Vrmz59PcnIyX3zxBRERETmumdy0aVN27NiB3W4H4N133+Xee+912e/hemh3sVJK\nqWtSu3Ztli9fDsD06dMB+OCDD0hISGDkyJHO40aOHMmPP/7Iu+++6+zuBfjpp5+oVq3aNaXVq1On\nDrVr1+a5555jyZIl+Pj4kJCQQPHixZ3HHD58mODgYCpWrAhAtWrVnO9t2LCB8ePHY4xh9+7dlC1b\n9obuPbc0yCqllLpmo0aNYvLkyc7X8fHxiIgz6IKVvL1mzZp06dKFwMBA2rVrx969e/nss8+oVq0a\nixcvJioqiilTphAXF5fjdapWrUqVKlWcr+12OwcPHiQoKCjbcXXr1mX79u15e5N5SLuLlVJKXbM3\n3niDqKgoDhw4QFRUFCNGjODFF18kKiqKLVu2sGfPHl5++WXAGnQUFxfH559/Ts2aNQG4//77KVOm\nDGlpaURHR9OvXz9n2RkZGSQlJTF06FBOnjyZ7bqbN29m5MiRPPHEE859npCpSIOsUkp5COPC7XrM\nnTuX/v37X7K/f//+LF682Pn6f//7H8uXL2fu3Ll8+eWXALRp04aPPvqIGjVq0LVrV+644w4AZsyY\nQcWKFXnwwQd5+OGHqVy5srOc8PBwhg8fzrlz51i7di0bN24E4OTJkwU+4bsGWaWU8gAi4vLtWnXr\n1o1du3Zx8OBB57nbt2/n5MmT2Vqmd911FwsXLqRDhw6MGzfOOT3n/PnzJCcns2PHDsaNGwfAo48+\nysmTJ1m4cCEPP/ywc25seno6d999N+XLl8dut7N27Vr8/PwAa0Ry/fr18+6X7AL6TFYppdQ1ycjI\noHr16gBcuHCB++67D7vdjojw6aefAnDbbbcB1vQa+Hulqnr16tG+fXsGDx7M9u3badSoEdWqVaNi\nxYocOXKEGjVqXHK9Bx54gEGDBvHSSy8hIqSlpdGkSRMaNGjA/v37mTlzJjt27Mifm88l4wl92kop\ndTMxxoinfzanpaVhs9kICAi44nExMTEEBgZed/k2m41ffvmF0NDQ3FYxc9Wp3K0nea3X8PS/SKWU\nKmwKQ5D1BPkRZPWZrFJKKeUiGmSVUkopF9Egq5RSSrmIji5WSqkCKLf5XVXBogOflFJKKRfR7mKl\nlFLKRTTIKqWUUi6iQVYppZRyEQ2ySimllItokFVKKaVcRIOsUkop5SIaZJVSSikX0SCrlFJKuYgG\nWaWUUspFNMgqpZRSLqJBVimllHKR/wf0zVM1VNyqqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f215c71ab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_index_title('十大成份股、行业构成') \n",
    "df=show_index_stocks(jy_code)\n",
    "pie_index_stock(df)\n",
    "df[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.关联基金\n",
    "- 指数对应的优质基金信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指数：000300.SH 沪深300 关联基金\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>名称</th>\n",
       "      <th>类别</th>\n",
       "      <th>start_date</th>\n",
       "      <th>最近交易日</th>\n",
       "      <th>收盘</th>\n",
       "      <th>最低</th>\n",
       "      <th>最高</th>\n",
       "      <th>开盘</th>\n",
       "      <th>成交量(万)</th>\n",
       "      <th>成交额(万)</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>涨跌幅(%)</th>\n",
       "      <th>单位净值</th>\n",
       "      <th>累计净值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>510360.SH</th>\n",
       "      <td>广发300</td>\n",
       "      <td>etf</td>\n",
       "      <td>2015-09-09</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.066</td>\n",
       "      <td>1.060</td>\n",
       "      <td>1.075</td>\n",
       "      <td>1.068</td>\n",
       "      <td>16.062</td>\n",
       "      <td>1717.706</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510300.SH</th>\n",
       "      <td>300ETF</td>\n",
       "      <td>etf</td>\n",
       "      <td>2012-05-28</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>3.390</td>\n",
       "      <td>3.372</td>\n",
       "      <td>3.424</td>\n",
       "      <td>3.402</td>\n",
       "      <td>149.124</td>\n",
       "      <td>50622.312</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>-0.767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160706.SZ</th>\n",
       "      <td>嘉实300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2005-10-17</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.961</td>\n",
       "      <td>3.329</td>\n",
       "      <td>318.049</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.628</td>\n",
       "      <td>0.961</td>\n",
       "      <td>2.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167901.SZ</th>\n",
       "      <td>华宸300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2013-06-03</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166007.SZ</th>\n",
       "      <td>中欧300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2010-08-16</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.246</td>\n",
       "      <td>1.246</td>\n",
       "      <td>1.246</td>\n",
       "      <td>1.246</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159919.SZ</th>\n",
       "      <td>300ETF</td>\n",
       "      <td>etf</td>\n",
       "      <td>2012-05-28</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>3.686</td>\n",
       "      <td>3.671</td>\n",
       "      <td>3.721</td>\n",
       "      <td>3.710</td>\n",
       "      <td>11.429</td>\n",
       "      <td>4227.851</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.950</td>\n",
       "      <td>3.703</td>\n",
       "      <td>1.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510310.SH</th>\n",
       "      <td>HS300ETF</td>\n",
       "      <td>etf</td>\n",
       "      <td>2013-03-25</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.441</td>\n",
       "      <td>1.433</td>\n",
       "      <td>1.452</td>\n",
       "      <td>1.452</td>\n",
       "      <td>4.025</td>\n",
       "      <td>580.280</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>-0.972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159925.SZ</th>\n",
       "      <td>南方300</td>\n",
       "      <td>etf</td>\n",
       "      <td>2013-04-11</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.421</td>\n",
       "      <td>1.408</td>\n",
       "      <td>1.434</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.125</td>\n",
       "      <td>17.573</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510390.SH</th>\n",
       "      <td>平安300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>3.411</td>\n",
       "      <td>3.365</td>\n",
       "      <td>3.411</td>\n",
       "      <td>3.400</td>\n",
       "      <td>3.184</td>\n",
       "      <td>1080.351</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.088</td>\n",
       "      <td>3.393</td>\n",
       "      <td>0.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510330.SH</th>\n",
       "      <td>华夏300</td>\n",
       "      <td>etf</td>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>3.674</td>\n",
       "      <td>3.648</td>\n",
       "      <td>3.697</td>\n",
       "      <td>3.686</td>\n",
       "      <td>31.981</td>\n",
       "      <td>11745.554</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>-0.572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163407.SZ</th>\n",
       "      <td>兴全300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2011-01-19</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.641</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.653</td>\n",
       "      <td>1.646</td>\n",
       "      <td>0.550</td>\n",
       "      <td>89.724</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160615.SZ</th>\n",
       "      <td>鹏华300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2009-05-08</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.381</td>\n",
       "      <td>1.381</td>\n",
       "      <td>1.389</td>\n",
       "      <td>1.389</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.290</td>\n",
       "      <td>1.394</td>\n",
       "      <td>1.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160807.SZ</th>\n",
       "      <td>长盛300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2010-09-06</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.115</td>\n",
       "      <td>1.115</td>\n",
       "      <td>1.115</td>\n",
       "      <td>1.115</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165309.SZ</th>\n",
       "      <td>建信300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2009-11-30</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>1.060</td>\n",
       "      <td>1.060</td>\n",
       "      <td>1.070</td>\n",
       "      <td>1.070</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.308</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510380.SH</th>\n",
       "      <td>国寿300</td>\n",
       "      <td>lof</td>\n",
       "      <td>2018-02-07</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 名称   类别 start_date       最近交易日     收盘     最低     最高     开盘  \\\n",
       "510360.SH     广发300  etf  2015-09-09 2018-08-31  1.066  1.060  1.075  1.068   \n",
       "510300.SH    300ETF  etf  2012-05-28 2018-08-31  3.390  3.372  3.424  3.402   \n",
       "160706.SZ     嘉实300  lof  2005-10-17 2018-08-31  0.955  0.951  0.963  0.961   \n",
       "167901.SZ     华宸300  lof  2013-06-03 2018-08-31    NaN    NaN    NaN    NaN   \n",
       "166007.SZ     中欧300  lof  2010-08-16 2018-08-31  1.246  1.246  1.246  1.246   \n",
       "159919.SZ    300ETF  etf  2012-05-28 2018-08-31  3.686  3.671  3.721  3.710   \n",
       "510310.SH  HS300ETF  etf  2013-03-25 2018-08-31  1.441  1.433  1.452  1.452   \n",
       "159925.SZ     南方300  etf  2013-04-11 2018-08-31  1.421  1.408  1.434  1.410   \n",
       "510390.SH     平安300  lof  2018-01-26 2018-08-31  3.411  3.365  3.411  3.400   \n",
       "510330.SH     华夏300  etf  2013-01-16 2018-08-31  3.674  3.648  3.697  3.686   \n",
       "163407.SZ     兴全300  lof  2011-01-19 2018-08-31  1.641  1.600  1.653  1.646   \n",
       "160615.SZ     鹏华300  lof  2009-05-08 2018-08-31  1.381  1.381  1.389  1.389   \n",
       "160807.SZ     长盛300  lof  2010-09-06 2018-08-31  1.115  1.115  1.115  1.115   \n",
       "165309.SZ     建信300  lof  2009-11-30 2018-08-31  1.060  1.060  1.070  1.070   \n",
       "510380.SH     国寿300  lof  2018-02-07 2018-08-31  0.915  0.915  0.915  0.915   \n",
       "\n",
       "            成交量(万)     成交额(万)    涨跌额  涨跌幅(%)   单位净值   累计净值  \n",
       "510360.SH   16.062   1717.706 -0.008  -0.750    NaN    NaN  \n",
       "510300.SH  149.124  50622.312 -0.026  -0.767    NaN    NaN  \n",
       "160706.SZ    3.329    318.049 -0.006  -0.628  0.961  2.852  \n",
       "167901.SZ      NaN        NaN    NaN     NaN    NaN    NaN  \n",
       "166007.SZ    0.000      0.000  0.000   0.000    NaN    NaN  \n",
       "159919.SZ   11.429   4227.851 -0.035  -0.950  3.703  1.415  \n",
       "510310.SH    4.025    580.280 -0.014  -0.972    NaN    NaN  \n",
       "159925.SZ    0.125     17.573 -0.002  -0.141    NaN    NaN  \n",
       "510390.SH    3.184   1080.351  0.003   0.088  3.393  0.812  \n",
       "510330.SH   31.981  11745.554 -0.021  -0.572    NaN    NaN  \n",
       "163407.SZ    0.550     89.724 -0.005  -0.305    NaN    NaN  \n",
       "160615.SZ    0.000      0.028  0.004   0.290  1.394  1.454  \n",
       "160807.SZ    0.000      0.000  0.000   0.000    NaN    NaN  \n",
       "165309.SZ    0.003      0.308 -0.006  -0.566    NaN    NaN  \n",
       "510380.SH    0.000      0.000  0.000   0.000    NaN    NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#指数对应的基金信息\n",
    "show_index_title('关联基金') \n",
    "df=show_index_fund(index_code)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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